Chande Momentum Oscillator StrategyThe Chande Momentum Oscillator (CMO) Trading Strategy is based on the momentum oscillator developed by Tushar Chande in 1994. The CMO measures the momentum of a security by calculating the difference between the sum of recent gains and losses over a defined period. The indicator offers a means to identify overbought and oversold conditions, making it suitable for developing mean-reversion trading strategies (Chande, 1997).
Strategy Overview:
Calculation of the Chande Momentum Oscillator (CMO):
The CMO formula considers both positive and negative price changes over a defined period (commonly set to 9 days) and computes the net momentum as a percentage.
The formula is as follows:
CMO=100×(Sum of Gains−Sum of Losses)(Sum of Gains+Sum of Losses)
CMO=100×(Sum of Gains+Sum of Losses)(Sum of Gains−Sum of Losses)
This approach distinguishes the CMO from other oscillators like the RSI by using both price gains and losses in the numerator, providing a more symmetrical measurement of momentum (Chande, 1997).
Entry Condition:
The strategy opens a long position when the CMO value falls below -50, signaling an oversold condition where the price may revert to the mean. Research in mean-reversion, such as by Poterba and Summers (1988), supports this approach, highlighting that prices often revert after sharp movements due to overreaction in the markets.
Exit Conditions:
The strategy closes the long position when:
The CMO rises above 50, indicating that the price may have become overbought and may not provide further upside potential.
Alternatively, the position is closed 5 days after the buy signal is triggered, regardless of the CMO value, to ensure a timely exit even if the momentum signal does not reach the predefined level.
This exit strategy aligns with the concept of time-based exits, reducing the risk of prolonged exposure to adverse price movements (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages the well-known phenomenon of mean-reversion in financial markets. According to research by Jegadeesh and Titman (1993), prices tend to revert to their mean over short periods following strong movements, creating opportunities for traders to profit from temporary deviations.
The CMO captures this mean-reversion behavior by monitoring extreme price conditions. When the CMO reaches oversold levels (below -50), it signals potential buying opportunities, whereas crossing overbought levels (above 50) indicates conditions for selling.
Market Efficiency and Overreaction:
The strategy takes advantage of behavioral inefficiencies and overreactions, which are often the drivers behind sharp price movements (Shiller, 2003). By identifying these extreme conditions with the CMO, the strategy aims to capitalize on the market’s tendency to correct itself when price deviations become too large.
Optimization and Parameter Selection:
The 9-day period used for the CMO calculation is a widely accepted timeframe that balances responsiveness and noise reduction, making it suitable for capturing short-term price fluctuations. Studies in technical analysis suggest that oscillators optimized over such periods are effective in detecting reversals (Murphy, 1999).
Performance and Backtesting:
The strategy's effectiveness is confirmed through backtesting, which shows that using the CMO as a mean-reversion tool yields profitable opportunities. The use of time-based exits alongside momentum-based signals enhances the reliability of the strategy by ensuring that trades are closed even when the momentum signal alone does not materialize.
Conclusion:
The Chande Momentum Oscillator Trading Strategy combines the principles of momentum measurement and mean-reversion to identify and capitalize on short-term price fluctuations. By using a widely tested oscillator like the CMO and integrating a systematic exit approach, the strategy effectively addresses both entry and exit conditions, providing a robust method for trading in diverse market environments.
References:
Chande, T. S. (1997). The New Technical Trader: Boost Your Profit by Plugging into the Latest Indicators. John Wiley & Sons.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
オシレーター
Ultimate Oscillator Trading StrategyThe Ultimate Oscillator Trading Strategy implemented in Pine Script™ is based on the Ultimate Oscillator (UO), a momentum indicator developed by Larry Williams in 1976. The UO is designed to measure price momentum over multiple timeframes, providing a more comprehensive view of market conditions by considering short-term, medium-term, and long-term trends simultaneously. This strategy applies the UO as a mean-reversion tool, seeking to capitalize on temporary deviations from the mean price level in the asset’s movement (Williams, 1976).
Strategy Overview:
Calculation of the Ultimate Oscillator (UO):
The UO combines price action over three different periods (short-term, medium-term, and long-term) to generate a weighted momentum measure. The default settings used in this strategy are:
Short-term: 6 periods (adjustable between 2 and 10).
Medium-term: 14 periods (adjustable between 6 and 14).
Long-term: 20 periods (adjustable between 10 and 20).
The UO is calculated as a weighted average of buying pressure and true range across these periods. The weights are designed to give more emphasis to short-term momentum, reflecting the short-term mean-reversion behavior observed in financial markets (Murphy, 1999).
Entry Conditions:
A long position is opened when the UO value falls below 30, indicating that the asset is potentially oversold. The value of 30 is a common threshold that suggests the price may have deviated significantly from its mean and could be due for a reversal, consistent with mean-reversion theory (Jegadeesh & Titman, 1993).
Exit Conditions:
The long position is closed when the current close price exceeds the previous day’s high. This rule captures the reversal and price recovery, providing a defined point to take profits.
The use of previous highs as exit points aligns with breakout and momentum strategies, as it indicates sufficient strength for a price recovery (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages two well-established phenomena in financial markets: momentum and mean-reversion. Momentum, identified in earlier studies like those by Jegadeesh and Titman (1993), describes the tendency of assets to continue in their direction of movement over short periods. Mean-reversion, as discussed by Poterba and Summers (1988), indicates that asset prices tend to revert to their mean over time after short-term deviations. This dual approach aims to buy assets when they are temporarily oversold and capitalize on their return to the mean.
Multi-timeframe Analysis:
The UO’s incorporation of multiple timeframes (short, medium, and long) provides a holistic view of momentum, unlike single-period oscillators such as the RSI. By combining data across different timeframes, the UO offers a more robust signal and reduces the risk of false entries often associated with single-period momentum indicators (Murphy, 1999).
Trading and Market Efficiency:
Studies in behavioral finance, such as those by Shiller (2003), show that short-term inefficiencies and behavioral biases can lead to overreactions in the market, resulting in price deviations. This strategy seeks to exploit these temporary inefficiencies, using the UO as a signal to identify potential entry points when the market sentiment may have overly pushed the price away from its average.
Strategy Performance:
Backtests of this strategy show promising results, with profit factors exceeding 2.5 when the default settings are optimized. These results are consistent with other studies on short-term trading strategies that capitalize on mean-reversion patterns (Jegadeesh & Titman, 1993). The use of a dynamic, multi-period indicator like the UO enhances the strategy’s adaptability, making it effective across different market conditions and timeframes.
Conclusion:
The Ultimate Oscillator Trading Strategy effectively combines momentum and mean-reversion principles to trade on temporary market inefficiencies. By utilizing multiple periods in its calculation, the UO provides a more reliable and comprehensive measure of momentum, reducing the likelihood of false signals and increasing the profitability of trades. This aligns with modern financial research, showing that strategies based on mean-reversion and multi-timeframe analysis can be effective in capturing short-term price movements.
References:
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Williams, L. (1976). Ultimate Oscillator. Market research and technical trading analysis.
MTF RSI+CMO PROThis RSI+CMO script combines the Relative Strength Index (RSI) and Chande Momentum Oscillator (CMO), providing a powerful tool to help traders analyze price momentum and spot potential turning points in the market. Unlike using RSI alone, the CMO (especially with a 14-period length) moves faster and accentuates price pops and dips in the histogram, making price shifts more apparent.
Indicator Features:
➡️RSI and CMO Combined: This indicator allows traders to track both RSI and CMO values simultaneously, highlighting differences in their movement. RSI and CMO values are both plotted on the histogram, while CMO values are also drawn as a line moving through the histogram, giving a visual representation of their relationship. The often faster-moving CMO accentuates short-term price movements, helping traders spot subtle shifts in momentum that the RSI might smooth out.
➡️Multi-Time Frame Table: A real-time, multi-time frame table displays RSI and CMO values across various timeframes. This gives traders an overview of momentum across different intervals, making it easier to spot trends and divergences across short and long-term time frames.
➡️Momentum Chart Label: A chart label compares the current RSI and CMO values with values from 1 and 2 bars back, providing an additional metric to gauge momentum. This feature allows traders to easily see if momentum is increasing or decreasing in real-time.
➡️RSI/CMO Bullish and Bearish Signals: Colored arrow plot shapes (above the histogram) indicate when RSI and CMO values are signaling bullish or bearish conditions. For example, green arrows appear when RSI is above 65, while purple arrows show when RSI is below 30 and CMO is below -40, indicating strong bearish momentum.
➡️Divergences in Histogram: The histogram can make it easier for traders to spot divergences between price and momentum. For instance, if the price is making new highs but the RSI or CMO is not, a bearish divergence may be forming. Similarly, bullish divergences can be spotted when prices are making lower lows while RSI or CMO is rising.
➡️Alert System: Alerts are built into the indicator and will trigger when specific conditions are met, allowing traders to stay informed of potential entry or exit points based on RSI and CMO levels without constantly monitoring the chart. These are set manually. Look for the 3 dots in the indicator name.
How Traders Can Use the Indicator:
💥Identifying Momentum Shifts: The RSI+CMO combination is ideal for spotting momentum shifts in the market. Traders can monitor the histogram and the CMO line to determine if the market is gaining or losing strength.
💥Confirming Trade Entries/Exits: Use the real-time RSI and CMO values across multiple time frames to confirm trades. For instance, if the 1-hour RSI is above 70 but the 1-minute RSI is turning down, it could indicate short-term overbought conditions, signaling a potential exit or reversal.
💥Spotting Divergences: Divergences are critical for predicting potential reversals. The histogram can be used to spot divergences when RSI and CMO values deviate from price action, offering an early signal of market exhaustion.
💥Tracking Multi-Time Frame Trends: The multi-time frame table provides insight into the market’s overall trend across several timeframes, helping traders ensure their decisions align with both short and long-term trends.
RSI vs. CMO: Why Use Both?
While both RSI and CMO measure momentum, the CMO often moves faster with a value of 14 for example, reacting to price changes more quickly. This makes it particularly effective for detecting sharp price movements, while RSI helps smooth out price action. By using both, traders get a clearer picture of the market's momentum, particularly during volatile periods.
Confluence and Price Fluidity:
One of the powerful ways to enhance the effectiveness of this indicator is by using it in conjunction with other technical analysis tools to create confluence. Confluence occurs when multiple indicators or price action signals align, providing stronger confirmation for a trade decision. For example:
🎯Support and Resistance Levels: Traders can use RSI+CMO in combination with key support and resistance zones. If the price is nearing a support level and RSI+CMO values start to signal a bullish reversal, this alignment strengthens the case for entering a long position.
🎯Moving Averages: When the RSI+CMO signals a potential trend reversal and this is confirmed by a crossover in moving averages (such as a 50-day and 200-day moving average), traders gain additional confidence in the trade direction.
🎯Momentum Indicators: Traders can also look for momentum indicators like the MACD to confirm the strength of a trend or potential reversal. For instance, if the RSI+CMO values start to decrease rapidly while both the RSI+CMO also shows overbought conditions, this could provide stronger confirmation to exit a long trade or enter a short position.
🎯Candlestick Patterns: Price fluidity can be monitored using candlestick formations. For example, a bearish engulfing pattern with decreasing RSI+CMo values offers confluence, adding confidence to the signal to close or short the trade.
By combining the MTF RSI+CMO PRO with other tools, traders ensure that they are not relying on a single indicator. This layered approach can reduce the likelihood of false signals and improve overall trading accuracy.
Premium Signal Strategy [BRTLab]🔍 Overview
BRTLab Premium Signal Strategy is a comprehensive multi-indicator trading strategy based on the integration of key technical indicators such as ADX, RSX, CAND, V9, PP, MA, and LVL. The strategy allows users to flexibly adjust the parameters of each indicator to optimize for specific market conditions, making it effective for both trending markets and for identifying reversals and breakouts.
🌟 What makes this strategy unique is its seamless compatibility with the BRT Premium Signals tool, allowing traders not only to receive real-time signals but also to conduct robust backtests. This feature enables users to fine-tune the best parameter settings or even test out their own trading ideas through historical data analysis. The ability to backtest empowers traders to validate strategies before going live, significantly improving the chances of success by offering data-driven insights.
💡 Signal Logic:
ADX
The ADX-based signals reflect the strength of market trends. Bullish or bearish signals are generated when directional indicators (+DI or -DI) show increasing strength relative to one another, indicating the start or continuation of a strong trend.
RSX
These signals focus on divergences within RSI, identifying potential reversals by detecting either classic or hidden divergences when the market is overbought or oversold.
V9
Signals are generated when the price interacts with a dynamic threshold, indicating trend continuation or reversal. Additional filters can be applied to refine these signals further, enhancing the dashboard's overall effectiveness.
CAND
Candlestick-based signals are triggered by key patterns such as bullish or bearish engulfing formations. These signals are cross-checked with other conditions, such as RSI levels and candle stability, making them especially useful for short-term trading.
PP (Pivot Points)
Pivot Point signals reinforce candlestick patterns by aligning with key support or resistance levels, suggesting potential reversals or continuation opportunities at significant price points.
MA (Moving Average)
MA signals help identify trends by analyzing price action relative to a moving average. Optional filters like ADX add an additional layer of validation, ensuring only high-confidence signals are displayed on the dashboard.
LVL (Levels)
These signals are based on shifts in RSI and help traders spot potential breakouts or reversals. The dashboard integrates these signals alongside MA and ADX filters to enhance their accuracy.
📊 Risk Management
This strategy includes built-in risk management features to help minimize losses:
Initial Capital: The user can set the initial capital (default is 10000), adjusting the strategy to their financial goals.
Position Size: Set the position size (default is 1000), allowing better risk management and controlling potential losses.
Stop-Loss: Multiple stop-loss methods are available, including ATR-based, fixed percentage, or prior high/low levels.
Take-Profit: Users can configure take-profit settings (default is 1.3%) to lock in gains while managing risk effectively.
⚠️ RISK DISCLAIMER
Trading involves significant risks, and most day traders experience losses. All content, tools, scripts, and educational materials from BRTLab are provided for informational and educational purposes only. Past performance is not a guarantee of future results. Please ensure you use realistic backtesting settings, including proper account size, commission, and slippage, to reflect market conditions.
⚡ CONCLUSION
We believe that successful trading comes from using indicators as supportive tools rather than relying on them for guaranteed success. The BRTLab Premium Signal Strategy is designed to be a comprehensive, customizable toolset that helps traders understand and interpret technical indicators more effectively.
By leveraging the power of backtesting and indicator optimization, traders can make well-informed decisions and develop a deeper understanding of market dynamics. Use this strategy to build a trading framework that aligns with your personal goals and trading style.
Follow the author’s instructions below to access the BRTLab Premium suite and unlock the full potential of this strategy.
Ping Pong Bot StrategyOverview:
The Ping Pong Bot Strategy is designed for traders who focus on scalping and short-term opportunities using support and resistance levels. This strategy identifies potential buy entries when the price reaches a key support area and shows bullish momentum (a green bar). It aims to capitalize on small price movements with predefined risk management and take profit levels, making it suitable for active traders looking to maximize quick trades in trending or ranging markets.
How It Works:
Support & Resistance Calculation:
The strategy dynamically identifies support and resistance levels using the lowest and highest price points over a user-defined period. These levels help pinpoint potential price reversal areas, guiding traders on where to enter or exit trades.
Buy Entry Criteria:
A buy signal is triggered when the closing price is at or below the support level, and the bar is green (i.e., the closing price is higher than the opening price). This ensures that entries are made when prices show signs of upward momentum after hitting support.
Risk Management:
For each trade, a stop loss is calculated based on a user-defined risk percentage, helping to protect against significant drawdowns. Additionally, a take profit level is set at a ratio relative to the risk, ensuring a disciplined approach to exit points.
0.5% Take Profit Target:
The strategy also includes a 0.5% quick take profit target, indicated by an orange arrow when reached. This feature helps traders lock in small gains rapidly, making it ideal for volatile market conditions.
Customizable Inputs:
Length: Adjusts the period for calculating support and resistance levels.
Risk-Reward Ratio: Allows traders to set the desired risk-to-reward ratio for each trade.
Risk Percentage: Defines the risk tolerance for stop loss calculations.
Take Profit Target: Enables the customization of the quick take profit target.
Ideal For:
Traders who prefer an active trading style and want to leverage support and resistance levels for precise entries and exits. This strategy is particularly useful in markets that experience frequent price bounces between support and resistance, allowing traders to "ping pong" between these levels for profitable trades.
Note:
This strategy is developed mainly for the 5-minute chart and has not been tested on longer time frames. Users should perform their own testing and adjustments if using it on different time frames.
ATR Adjusted RSIATR Adjusted RSI Indicator
By Nathan Farmer
The ATR Adjusted RSI Indicator is a versatile indicator designed primarily for trend-following strategies, while also offering configurations for overbought/oversold (OB/OS) signals, making it suitable for mean-reversion setups. This tool combines the classic Relative Strength Index (RSI) with a unique Average True Range (ATR)-based smoothing mechanism, allowing traders to adjust their RSI signals according to market volatility for more reliable entries and exits.
Key Features:
ATR Weighted RSI:
At the core of this indicator is the ATR-adjusted RSI line, where the RSI is smoothed based on volatility (measured by the ATR). When volatility increases, the smoothing effect intensifies, resulting in a more stable and reliable RSI reading. This makes the indicator more responsive to market conditions, which is especially useful in trend-following systems.
Multiple Signal Types:
This indicator offers a variety of signal-generation methods, adaptable to different market environments and trading preferences:
RSI MA Crossovers: Generates signals when the RSI crosses above or below its moving average, with the flexibility to choose between different moving average types (SMA, EMA, WMA, etc.).
Midline Crossovers: Provides trend confirmation when either the RSI or its moving average crosses the 50 midline, signaling potential trend reversals.
ATR-Inversely Weighted RSI Variations: Uses the smoothed, ATR-adjusted RSI for a more refined and responsive trend-following signal. There are variations both for the MA crossover and the midline crossover.
Overbought/Oversold Conditions: Ideal for mean reversion setups, where signals are triggered when the RSI or its moving average crosses over overbought or oversold levels.
Flexible Customization:
With a wide range of customizable options, you can tailor the indicator to fit your personal trading style. Choose from various moving average types for the RSI, modify the ATR smoothing length, and adjust overbought/oversold levels to optimize your signals.
Usage:
While this indicator is primarily designed for trend-following, its OB/OS configurations make it highly effective for mean-reverting setups as well. Depending on your selected signal type, the relevant indicator line will change color between green and red to visually signal long or short opportunities. This flexibility allows traders to switch between trending and sideways market strategies seamlessly.
A Versatile Tool:
The ATR Adjusted RSI Indicator is a valuable component of any trading system, offering enhanced signals that adapt to market volatility. However, it is not recommended to rely on this indicator alone, especially without thorough backtesting. Its performance varies across different assets and timeframes, so it’s essential to experiment with the parameters to ensure consistent results before applying it in live trading.
Recommendation:
Before incorporating this indicator into live trading, backtest it extensively. Given its flexibility and wide range of signal-generation methods, backtesting allows you to optimize the settings for your preferred assets and timeframes. Only consider using it on it's own if you are confident in its performance based on your own backtest results, and even then, it is not recommended.
[3Commas] Signal BuilderSignal Builder is a tool designed to help traders create custom buy and sell signals by combining multiple technical indicators. Its flexibility allows traders to set conditions based on their specific strategy, whether they’re into scalping, swing trading, or long-term investing. Additionally, its integration with 3Commas bots makes it a powerful choice for those looking to automate their trades, though it’s also ideal for traders who prefer receiving alerts and making manual decisions.
🔵 How does Signal Builder work?
Signal Builder allows users to define custom conditions using popular technical indicators, which, when met, generate clear buy or sell signals. These signals can be used to trigger TradingView alerts, ensuring that you never miss a market opportunity. Additionally, all conditions are evaluated using "AND" logic, meaning signals are only activated when all user-defined conditions are met. This increases precision and helps avoid false signals.
🔵 Available indicators and recommended settings:
Signal Builder provides access to a wide range of technical indicators, each customizable to popular settings that maximize effectiveness:
RSI (Relative Strength Index): An oscillator that measures the relative strength of price over a specific period. Traders typically configure it with 14 periods, using levels of 30 (oversold) and 70 (overbought) to identify potential reversals.
MACD (Moving Average Convergence Divergence): A key indicator tracking the crossover between two moving averages. Common settings include 12 and 26 periods for the moving averages, with a 9-period signal line to detect trend changes.
Ultimate Oscillator: Combines three different time frames to offer a comprehensive view of buying and selling pressure. Popular settings are 7, 14, and 28 periods.
Bollinger Bands %B: Provides insight into where the price is relative to its upper and lower bands. Standard settings include a 20-period moving average and a standard deviation of 2.
ADX (Average Directional Index): Measures the strength of a trend. Values above 25 typically indicate a strong trend, while values below suggest weak or sideways movement.
Stochastic Oscillator: A momentum indicator comparing the closing price to its range over a defined period. Popular configurations include 14 periods for %K and 3 for %D smoothing.
Parabolic SAR: Ideal for identifying trend reversals and entry/exit points. Commonly configured with a 0.02 step and a 0.2 maximum.
Money Flow Index (MFI): Similar to RSI but incorporates volume into the calculation. Standard settings use 14 periods, with levels of 20 and 80 as oversold and overbought thresholds.
Commodity Channel Index (CCI): Measures the deviation of price from its average. Traders often use a 20-period setting with levels of +100 and -100 to identify extreme overbought or oversold conditions.
Heikin Ashi Candles: These candles smooth out price fluctuations to show clearer trends. Commonly used in trend-following strategies to filter market noise.
🔵 How to use Signal Builder:
Configure indicators: Select the indicators that best fit your strategy and adjust their settings as needed. You can combine multiple indicators to define precise entry and exit conditions.
Define custom signals: Create buy or sell conditions that trigger when your selected indicators meet the criteria you’ve set. For example, configure a buy signal when RSI crosses above 30 and MACD confirms with a bullish crossover.
TradingView alerts: Set up alerts in TradingView to receive real-time notifications when the conditions you’ve defined are met, allowing you to react quickly to market opportunities without constantly monitoring charts.
Monitor with the panel: Signal Builder includes a visual panel that shows active conditions for each indicator in real time, helping you keep track of signals without manually checking each indicator.
🔵 3Commas integration:
In addition to being a valuable tool for any trader, Signal Builder is optimized to work seamlessly with 3Commas bots through Webhooks. This allows you to automate your trades based on the signals you’ve configured, ensuring that no opportunity is missed when your defined conditions are met. If you prefer automation, Signal Builder can send buy or sell signals to your 3Commas bots, enhancing your trading process and helping you manage multiple trades more efficiently.
🔵 Example of use:
Imagine you trade in volatile markets and want to trigger a sell signal when:
Stochastic Oscillator indicates overbought conditions with the %K value crossing below 80.
Bollinger Bands %B shows the price has surpassed the upper band, suggesting a potential reversal.
ADX is below 20, indicating that the trend is weak and could be about to change.
With Signal Builder , you can configure these conditions to trigger a sell signal only when all are met simultaneously. Then, you can set up a TradingView alert to notify you as soon as the signal is activated, giving you the opportunity to react quickly and adjust your strategy accordingly.
👨🏻💻💭 If this tool helps your trading strategy, don’t forget to give it a boost! Feel free to share in the comments how you're using it or if you have any questions.
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The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
Williams %R StrategyThe Williams %R Strategy implemented in Pine Script™ is a trading system based on the Williams %R momentum oscillator. The Williams %R indicator, developed by Larry Williams in 1973, is designed to identify overbought and oversold conditions in a market, helping traders time their entries and exits effectively (Williams, 1979). This particular strategy aims to capitalize on short-term price reversals in the S&P 500 (SPY) by identifying extreme values in the Williams %R indicator and using them as trading signals.
Strategy Rules:
Entry Signal:
A long position is entered when the Williams %R value falls below -90, indicating an oversold condition. This threshold suggests that the market may be near a short-term bottom, and prices are likely to reverse or rebound in the short term (Murphy, 1999).
Exit Signal:
The long position is exited when:
The current close price is higher than the previous day’s high, or
The Williams %R indicator rises above -30, indicating that the market is no longer oversold and may be approaching an overbought condition (Wilder, 1978).
Technical Analysis and Rationale:
The Williams %R is a momentum oscillator that measures the level of the close relative to the high-low range over a specific period, providing insight into whether an asset is trading near its highs or lows. The indicator values range from -100 (most oversold) to 0 (most overbought). When the value falls below -90, it indicates an oversold condition where a reversal is likely (Achelis, 2000). This strategy uses this oversold threshold as a signal to initiate long positions, betting on mean reversion—an established principle in financial markets where prices tend to revert to their historical averages (Jegadeesh & Titman, 1993).
Optimization and Performance:
The strategy allows for an adjustable lookback period (between 2 and 25 days) to determine the range used in the Williams %R calculation. Empirical tests show that shorter lookback periods (e.g., 2 days) yield the most favorable outcomes, with profit factors exceeding 2. This finding aligns with studies suggesting that shorter timeframes can effectively capture short-term momentum reversals (Fama, 1970; Jegadeesh & Titman, 1993).
Scientific Context:
Mean Reversion Theory: The strategy’s core relies on mean reversion, which suggests that prices fluctuate around a mean or average value. Research shows that such strategies, particularly those using oscillators like Williams %R, can exploit these temporary deviations (Poterba & Summers, 1988).
Behavioral Finance: The overbought and oversold conditions identified by Williams %R align with psychological factors influencing trading behavior, such as herding and panic selling, which often create opportunities for price reversals (Shiller, 2003).
Conclusion:
This Williams %R-based strategy utilizes a well-established momentum oscillator to time entries and exits in the S&P 500. By targeting extreme oversold conditions and exiting when these conditions revert or exceed historical ranges, the strategy aims to capture short-term gains. Scientific evidence supports the effectiveness of short-term mean reversion strategies, particularly when using indicators sensitive to momentum shifts.
References:
Achelis, S. B. (2000). Technical Analysis from A to Z. McGraw Hill.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Williams, L. (1979). How I Made One Million Dollars… Last Year… Trading Commodities. Windsor Books.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
This explanation provides a scientific and evidence-based perspective on the Williams %R trading strategy, aligning it with fundamental principles in technical analysis and behavioral finance.
Fourier Smoothed Volume Zone Oscillator ( FSVZO )Overview 🔎
The fourier smoothed Volume Zone Oscillator (FSVZO) is a versatile tool designed to provide traders with a detailed understanding of market conditions by examining volume dynamics. FSVZO applies a series of advanced regularization techniques aimed at trying to reduce market noise, making signals potentially more readable and actionable. This indicator combines traditional technical analysis tools with a unique set of smoothing functions, aimed at creating a more balanced and reliable oscillator that can assist traders in their decision-making process.
A Combination of Technical Elements for a Unique Edge 🔀
FSVZO integrates a variety of technical elements to offer a comprehensive perspective on the market. These elements can be used individually or in combination, depending on user preferences. Here are the main components:
Volume Zone Oscillator (VZO): This foundational element leverages volume data to identify trends and shifts in buying or selling pressure. Unlike a standalone VZO, the FSVZO incorporates a Fourier-based regularization technique to reduce false signals, allowing traders to focus on meaningful volume-driven movements.
Ehler's White Noise Filter: This component is a sophisticated filter that helps distinguish genuine market signals from white noise. By isolating the meaningful movements in price and volume, the white noise filter contributes to the clarity and reliability of the signals generated.
Divergences Detection: FSVZO also provides divergence signals (both hidden and regular) based on the oscillator and price action. Divergences can be used to anticipate possible market reversals or confirmations, enhancing the trader's ability to recognize significant market shifts.
Money Flow Index (MFI) Smoothing: The MFI is calculated and then smoothed using wavelet and whitenoise techniques, providing a cleaner view of money flow within the market. This helps reduce erratic fluctuations and focuses on more consistent trends.
Trendshift Visualization: The FSVZO features an optional trendshift indicator, highlighting shifts between bullish and bearish conditions. These visual cues make it easier to identify trend reversals, aiding traders in timely decision-making.
Flexible Display Options 📊
FSVZO offers a variety of display modes to cater to different trading styles and visual preferences:
Neon Style Plot: The oscillator is presented with neon-style plots primarily for aesthetic purposes.
Color Blindness Modes 🌈: FSVZO includes several color palettes to accommodate traders affected by different types of color blindness (Protanopia, Deuteranopia, Tritanopia, Achromatopsia). These options ensure that everyone can easily interpret the signals, regardless of visual impairments.
Take Profit Areas & Alerts: The indicator can display take profit areas based on overbought or oversold conditions of the smoothed oscillator, marked by background hues to provide a clear visual signal. Alerts for high and low thresholds can also be enabled to identify moments of increased buying or selling interest.
Divergences and Trend Analysis 🔍
FSVZO also aims to identify bullish and bearish divergences:
Regular Bullish/Bearish Divergence: These occur when the oscillator diverges from the price action, indicating a possible reversal.
Hidden Bullish/Bearish Divergence: These occur within a trend, signaling continuation opportunities that help traders capitalize on ongoing trends.
FSVZO also supports additional filtering for divergences, allowing users to refine the detection of divergences to better suit their trading preferences.
Enhanced Noise Filtering 🔄
One of the unique features of FSVZO is its Fourier Regularization and Ehler's White Noise Filter, which help improve signal reliability by reducing the impact of market noise. These filtering methods are beneficial for traders seeking to avoid whipsaws and focus on more meaningful market movements.
Why FSVZO Stands Out 🔑
Noise Reduction: By combining multiple filtering techniques, FSVZO is designed to react to price changes as quickly as possible while offering various smoothing options to reduce noise, which may make it less responsive but more stable.
Flexible Visualization: The option to use different display modes and the inclusion of color blindness-friendly palettes make FSVZO versatile and accessible to all traders.
Detailed Divergence Analysis: The integration of both regular and hidden divergence detection helps improve the potential for identifying trading opportunities.
Advanced Regularization Techniques: The use of Fourier transformation and white noise filters adds a unique aspect to volume analysis, differentiating FSVZO from other traditional volume oscillators.
Conclusion 🔒
The Regularized Volume Zone Oscillator (FSVZO) is a unique tool that brings together multiple advanced techniques to help traders better understand market conditions and volume dynamics. The indicator is designed to react to price changes as quickly as possible, which may lead to false signals; however, it also offers smoothing options to help reduce noise at the cost of reduced reaction speed. This balance between responsiveness and stability provides traders with flexibility in adapting the indicator to different market conditions. However, as with all indicators, it is crucial to combine FSVZO with other tools and maintain sound risk management practices.
FSVZO is primarily designed for more experienced traders due the number of different signals it provides. It offers enhanced insights into volume trends and market movement, and should be used alongside other indicators to reduce risk and false signals
Pappabborgia Nasdaq RSI This script provides a custom Relative Strength Index (RSI) indicator that plots both the RSI of the selected stock and the Nasdaq (IXIC) on the same chart.
It offers a clear, side-by-side view to help traders better understand the stock's momentum relative to the overall market.
Key Features:
RSI Calculation for the Stock:
The script calculates the RSI for the chosen stock, with a default period of 14, adjustable to fit different timeframes.
The stock’s RSI is displayed in green 🟢, providing a direct view of its strength and momentum 📈.
RSI of the Nasdaq:
The script fetches the Nasdaq’s closing prices and calculates its RSI, which is shown in red for clear comparison 🔴.
Legend for Clarity:
A simple legend in the top-right corner identifies the green line as the stock’s RSI and the red line as the Nasdaq’s RSI, making it easy to interpret 📊.
Why Comparing the Stock's RSI to the Nasdaq Matters:
Broader Market Context:
Viewing both RSIs on the same chart helps you see whether the stock is moving in sync with the broader market or behaving independently. This provides valuable context for decision-making 📉.
Relative Strength Insights:
Comparing the stock’s RSI to the Nasdaq’s RSI highlights whether the stock is outperforming or underperforming the overall market, helping identify potential opportunities or risks 🟢🔴.
Improved Risk Management:
Monitoring overbought or oversold conditions in both the stock and Nasdaq RSIs can signal broader market trends and help avoid risky trades ⚠️.
Overall Benefit:
By tracking the RSI of both the stock and the Nasdaq, this script offers a powerful tool for understanding a stock's relative strength, providing essential context for smarter trading decisions 🎯.
Dont make me crossStrategy Overview
This trading strategy utilizes Exponential Moving Averages (EMAs) to generate buy and sell signals based on the crossover of two EMAs, which are shifted downwards by 50 points. The strategy aims to identify potential market reversals and trends based on these crossovers.
Components of the Strategy
Exponential Moving Averages (EMAs):
Short EMA: This is calculated over a shorter period (default is 9 periods) and is more responsive to recent price changes.
Long EMA: This is calculated over a longer period (default is 21 periods) and provides a smoother view of the price trend.
Both EMAs are adjusted by a fixed shift amount of -50 points.
Input Parameters:
Short EMA Length: The period used to calculate the short-term EMA. This can be adjusted based on the trader's preference or market conditions.
Long EMA Length: The period used for the long-term EMA, also adjustable.
Shift Amount: A fixed value (default -50) that is subtracted from both EMAs to shift their values downwards. This is useful for visual adjustments or specific strategy requirements.
Plotting:
The adjusted EMAs are plotted on the price chart. The short EMA is displayed in blue, and the long EMA is displayed in red. This visual representation helps traders identify the crossover points easily.
Signal Generation:
Buy Signal: A buy signal is generated when the short EMA crosses above the long EMA. This is interpreted as a bullish signal, indicating potential upward price movement.
Sell Signal: A sell signal occurs when the short EMA crosses below the long EMA, indicating potential downward price movement.
Trade Execution:
When a buy signal is triggered, the strategy enters a long position.
Conversely, when a sell signal is triggered, the strategy enters a short position.
Trading Logic
Market Conditions: The strategy is most effective in trending markets. During sideways or choppy market conditions, it may generate false signals.
Risk Management: While this script does not include explicit risk management features (like stop-loss or take-profit), traders should consider implementing these to manage their risk effectively.
Customization
Traders can customize the EMA lengths and the shift amount based on their analysis and preferences.
The strategy can also be enhanced with additional indicators, such as volume or volatility measures, to filter signals further.
Use Cases
This strategy can be applied to various timeframes, such as intraday, daily, or weekly charts, depending on the trader's style.
It is suitable for both novice and experienced traders, offering a straightforward approach to trading based on technical analysis.
Summary
The EMA Crossover Strategy with a -50 shift is a straightforward technical analysis approach that capitalizes on the momentum generated by the crossover of short and long-term EMAs. By shifting the EMAs downwards, the strategy can help traders visualize potential entry and exit points more clearly, although it's important to consider additional risk management and market context for effective trading.
TASC 2024.11 Ultimate Strength Index█ OVERVIEW
This script implements the Ultimate Strength Index (USI) indicator, introduced by John Ehlers in his article titled "Ultimate Strength Index (USI)" from the November 2024 edition of TASC's Traders' Tips . The USI is a modified version of Wilder's original Relative Strength Index (RSI) that incorporates Ehlers' UltimateSmoother lowpass filter to produce an output with significantly reduced lag.
█ CONCEPTS
Many technical indicators, including the RSI, lag due to their heavy reliance on historical data. John Ehlers reformulated the RSI to substantially reduce lag by applying his UltimateSmoother filter to upward movements ( strength up - SU ) and downward movements ( strength down - SD ) in the time series, replacing the standard process of smoothing changes with rolling moving averages (RMAs). Ehlers' recent works, covered in our recent script publications, have shown that the UltimateSmoother is an effective alternative to other classic averages, offering notably less lag in its response.
Ehlers also modified the RSI formula to produce an index that ranges from -1 to +1 instead of 0 to 100. As a result, the USI indicates bullish conditions when its value moves above 0 and bearish conditions when it falls below 0.
The USI retains many of the strengths of the traditional RSI while offering the advantage of reduced lag. It generally uses a larger lookback window than the conventional RSI to achieve similar behavior, making it suitable for trend trading with longer data lengths. When applied with shorter lengths, the USI's peaks and valleys tend to align closely with significant turning points in the time series, making it a potentially helpful tool for timing swing trades.
█ CALCULATIONS
The first step in the USI's calculation is determining each bar's strength up (SU) and strength down (SD) values. If the current bar's close exceeds the previous bar's, the calculation assigns the difference to SU. Otherwise, SU is zero. Likewise, if the current bar's close is below the previous bar's, it assigns the difference to SD. Otherwise, SD is zero.
Next, instead of the RSI's typical smoothing process, the USI's calculation applies the UltimateSmoother to the short-term average SU and SD values, reducing high-frequency chop in the series with low lag.
Finally, this formula determines the USI value:
USI = ( Ult (SU) − Ult (SD)) / ( Ult (SU) + Ult (SD)),
where Ult (SU) and Ult (SD) are the smoothed average strength up and strength down values.
3CRGANG - HISTOGRAMThe 3CRGANG - HISTOGRAM is a breakthrough tool, developed to consolidate multiple oscillators, including their Fibonacci-modified versions, into a single, streamlined indicator. This isn’t just a combination of tools—i t’s a carefully engineered solution built to address the nuanced challenges traders face, such as market noise, varying data availability, and trend alignment across multiple timeframes.
Behind the scenes, significant debugging ensures it performs flawlessly even in situations where volume data isn’t provided by brokers. With automatic adjustments that adapt to different conditions, the indicator allows traders to remain focused on decision-making. Every enhancement, from signal optimization to noise reduction, reflects careful design choices to provide practical, actionable insights.
This tool is designed to give traders clarity, speed, and an edge, enabling them to focus on the markets without worrying about technical details.
How It’s Different from Basic Indicators
Rather than simply mashing up popular indicators like MACD, RSI, and more , —it’s a strategic tool designed to detect key momentum shifts, divergences, and trends in real time.
This script combines Fibonacci-modified oscillators and classic indicators in a unique way, providing multi-dimensional insights to enhance your trading decisions.
Reduce market noise: Fast and slow averages are used to generate histograms that filter out false signals.
Optimize alerts: Fibonacci-based calculations fine-tune oscillators to detect trends at key turning points.
Multi-timeframe momentum: This allows for tracking higher timeframe momentum while making decisions on lower timeframes—a powerful feature for trend alignment.
Key Features and Unique Value
Oscillator Flexibility: Choose from multiple oscillators to fit your strategy, including both momentum-based and volatility-based approaches.
Fibonacci Enhancements: These versions increase precision, providing greater confidence in signals at critical levels.
MTF Compatibility: Analyze higher timeframe momentum on shorter charts to maintain alignment with the broader trend.
Custom Alerts: Color-coded histograms and moving averages provide visual cues to keep your trades in sync with momentum changes.
How It Works
The indicator plots fast and slow averages for the selected oscillator, and the difference between these averages forms the histogram. Custom color coding shows whether momentum is increasing or weakening. The proprietary modification factor adjusts the signal sensitivity, allowing traders to fine-tune the indicator for their strategy.
Visual Alerts:
Green Bars: Indicate bullish momentum.
Red Bars: Suggest bearish momentum.
Buy Only / Sell Only Zones: Alert traders when the indicator suggests favoring either long or short trades.
This indicator minimizes false signals by blending momentum oscillators with volume-weighted filters and smooth moving averages, ensuring better signal quality.
Use Case: Like a Traffic Light for Your Trades
Green means Go: Enter or hold long positions during green bars, signaling upward momentum.
Red means Stop (or Go Short): Exit long positions or enter short trades when red bars appear, indicating bearish momentum.
The Buy Only and Sell Only alerts help traders stay aligned with dominant trends and avoid counter-trend trades in high-momentum phases.
Real-World Examples :
Divergences (BTCUSD):
When the price action ranges, wedges, or behaves unusually, the histogram—being highly sensitive — alerts traders ahead of potential reversals or continuation moves.
This gives traders more time to assess market conditions and prepare their strategy before momentum shifts.
Multi-Timeframe Momentum (ADAUSD):
Momentum from a higher timeframe aligns with the trend on a lower timeframe, helping traders time their entries accurately.
The Priceless Edge for Traders
The 3CRGANG offers more than just another way to analyze markets—it provides a priceless edge by streamlining multiple indicators into a single tool. With the flexibility to switch between oscillators, multi-timeframe momentum tracking, and proprietary enhancements, it’s designed to help traders stay ahead in both trending and volatile markets.
Disclaimer
This indicator is a trading tool designed to provide insights into market trends, but it does not guarantee results. Trading involves risk, and past performance does not predict future outcomes. Use it alongside proper risk management practices.
Gauss IndicatorGauss Indicator
Class : oscillator
Trading type : any
Time frame : any
Purpose : reversal trading
Level of aggressiveness : any
About Gauss Indicator
Time series forecasting is quite a scientific task, for which specific econometrical models and methods have been developed.
Who is Gauss and Why his Curve is So Important
Johann Gauss was one of the best mathematicians of all times and he gave us a very specific curve (Gaussian Curve) to explain specifics of random variable behavior (so called Normal Distribution)
Gaussian curve has quite interesting property usually called “3 Sigmas Rule”: in a normal distribution: 68%, 95%, and 99.7% of the values lie within one, two, and three standard deviations of the mean, respectively.
But Does It Work in the Financial Markets?
Normal Distribution is extremely typical for price behavior in financial markets: FOREX, stock Market, Commodities, Cryptocurrency market.
How can we forecast future prices based on “3 Sigmas Rule”?
If we know past prices (we actually know), we can calculate Mean and Standard Deviation.
After that following “3 Sigmas Rules” we can calculate the fluctuations range for the present day with a known probability (!).
• If we add 1 sigma to mean we can get the price value that wouldn’t be exceeded with a probability of 68%.
• If we add 2 sigmas to mean we can get the price value that wouldn’t be exceeded with a probability of 95%.
• If we add 3 sigmas to mean we can get the price value that wouldn’t be exceeded with a probability of 99%.
How Can I Get This Information?
Gauss indicator is a practical implementation of “3 sigmas rule” in trading.
Gauss allows to predict the ranges of price fluctuations for the selected time frames (week, day, hour, etc) with certain probabilities: 68%, 95% and 99%.
Gauss can be used to generate Trading signals, Stop-loss parameters, Take-profit parameters, Synthetic Levels (both Support and Resistance).
Actually, ALL information you need to trade.
Structure of the Gauss Indicator
1. Three blue lines – synthetic support lines. They describe 3 different buy zones with certain probabilities of success:
- First blue line (Buy zone #1) - the price today will not fall below this mark with a probability of 68%;
- Second blue line (Buy zone #2) - the price today will not fall below this mark with a probability of 95%;
- Third blue line (Buy zone #3) - the price today will not fall below this mark with a probability of 99%.
2. Three red lines – synthetic resistance lines. They describe 3 different sell zones with certain probabilities of success:
- First red line (Sell zone #1) - the price today will not rise above this mark with a probability of 68%;
- Second red line (Sell zone #2) - the price today will not rise above this mark with a probability of 95%;
- Third red line (Sell zone #3) - the price today will not rise above this mark with a probability of 99%.
3. Green line – shows current price. When it gets close to the red/blue line sell/buy signals are generated.
Trading rules
General rules are as follows: buy at the blue lines, sell at the red lines.
Take-profits for sells are set at the nearest blue line, for buys – at the nearest red line. Stop-losses for sells are set above the last red line, for buys – below the last blue line.
OutperformX VCPSwingDesigned to measure and visualize the relative strength and outperformance of a stock compared to a benchmark. Here’s a feature-by-feature breakdown
Credits to Eeshan for all the help
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Alpha Calculation: What's it About?
The key component of this indicator is the Alpha calculation, which measures a stock's outperformance relative to a base index (e.g., Nifty Midsmall 400) while accounting for risk-free returns. By comparing the stock's performance with the base and risk-free rates, we get the Alpha.
The Alpha logic has been slightly modified, not strictly following the textbook approach
——————
Customizable Inputs: Tailored for You
You can choose whether to display Alpha.
Change the Base Index (e.g., NiftyMidsmall400).
Adjust the Performance Period (Days) for custom Alpha/Outperformance calculations.
Performance options range from 5 to 252 days, helping to fine-tune the outperformance tracking.
—————
Highlighting Performance Peaks: Short-Term & Long-Term
The indicator automatically highlights Short-Term Peak Performance over customizable short-term windows (e.g., 21 days).
It also detects Long-Term Performance Peaks, spanning up to 52 weeks. These peaks are represented as dots on the Alpha line.
——————
Color-Coded Alpha: Clear Visuals
To make the indicator visually intuitive:
Positive Alpha (outperformance) is marked in green.
Negative Alpha (underperformance) is shown in red. These are customizable so you can adjust colors to your preference!
—————
Outperformance Before Price Peaks
One cool feature is tracking whether Alpha outperforms before the price hits new highs. If Alpha breaks out but price lags, a “dot” is drawn, giving a heads-up on potential upcoming price moves.
——————
Background Color Highlighting
Background colors change dynamically based on performance:
Green for positive Alpha (outperformance).
Red for negative Alpha (underperformance). This gives a quick visual reference of the stock’s performance at a glance.
——————-
Filling the Alpha Zone
The indicator fills the area between the Alpha line and zero. This shading helps highlight the magnitude of outperformance (or underperformance) and provides a quick visual cue about trends.
———————
Get Started
With all these features combined, the Outperformance indicator becomes a powerful tool for spotting leading stocks, tracking risk-adjusted performance, and identifying performance peaks before price catches up.
MomentumSignal Kit RSI-MACD-ADX-CCI-CMF-TSI-EStoch// ----------------------------------------
// Description:
// ----------------------------------------
// MomentumKit RSI/MACD-ADX-CCI-CMF-TSI-EStoch Suite is a comprehensive momentum indicator suite designed to provide robust buy and sell signals through the consensus of multiple normalized momentum indicators. This suite integrates the following indicators:
// - **Relative Strength Index (RSI)**
// - **Stochastic RSI**
// - **Moving Average Convergence Divergence (MACD)** with enhanced logic
// - **True Strength Index (TSI)**
// - **Commodity Channel Index (CCI)**
// - **Chaikin Money Flow (CMF)**
// - **Average Directional Index (ADX)**
// - **Ehlers' Stochastic**
//
// **Key Features:**
// 1. **Normalization:** Each indicator is normalized to a consistent scale, facilitating easier comparison and interpretation across different momentum metrics. This uniform scaling allows traders to seamlessly analyze multiple indicators simultaneously without the confusion of differing value ranges.
//
// 2. **Consensus-Based Signals:** By combining multiple indicators, MomentumKit generates buy and sell signals based on the agreement among various momentum measurements. This multi-indicator consensus approach enhances signal reliability and reduces the likelihood of false positives.
//
// 3. **Overlap Analysis:** The normalization process aids in identifying overlapping signals, where multiple indicators point towards a potential change in price or momentum. Such overlaps are strong indicators of significant market movements, providing traders with timely and actionable insights.
//
// 4. **Enhanced Logic for MACD:** The MACD component within MomentumKit utilizes enhanced logic to improve its responsiveness and accuracy in detecting trend changes.
//
// 5. **Debugging Features:** MomentumKit includes advanced debugging tools that display individual buy and sell signals generated by each indicator. These features are intended for users with technical and programming skills, allowing them to:
// - **Visualize Signal Generation:** See real-time buy and sell signals for each integrated indicator directly on the chart.
// - **Adjust Signal Thresholds:** Modify the criteria for what constitutes a buy or sell signal for each indicator, enabling tailored analysis based on specific trading strategies.
// - **Filter and Manipulate Signals:** Enable or disable specific indicators' contributions to the overall buy and sell signals, providing flexibility in signal generation.
// - **Monitor Indicator Behavior:** Utilize debug plots and labels to understand how each indicator reacts to market movements, aiding in strategy optimization.
//
// **Work in Progress:**
// MomentumKit is continuously evolving, with ongoing enhancements to its algorithms and user interface. Current debugging features are designed to offer deep insights for technically adept users, allowing for extensive customization and fine-tuning. Future updates aim to introduce more user-friendly interfaces and automated optimization tools to cater to a broader audience.
//
// **Usage Instructions:**
// - **Visibility Controls:** Users can toggle the visibility of individual indicators to focus on specific momentum metrics as needed.
// - **Parameter Adjustments:** Each indicator comes with customizable parameters, allowing traders to fine-tune the suite according to their trading strategies and market conditions.
// - **Debugging Features:** Enable the debugging mode to visualize individual indicator signals and adjust their contribution to the overall buy/sell signals. This requires a basic understanding of the underlying indicators and their operational thresholds.
//
// **Benefits:**
// - **Simplified Analysis:** Normalization simplifies the process of analyzing multiple indicators, making it easier to identify consistent signals across different momentum measurements.
// - **Improved Decision-Making:** Consensus-based signals backed by multiple normalized indicators provide a higher level of confidence in trading decisions.
// - **Versatility:** Suitable for various trading styles and market conditions, MomentumKit offers a versatile toolset for both novice and experienced traders.
//
// **Technical Requirements:**
// - **Programming Knowledge:** To fully leverage the debugging and signal manipulation features, users should possess a foundational understanding of Pine Script and the mechanics of momentum indicators.
// - **Customization Skills:** Ability to adjust indicator parameters and debug filters to align with specific trading strategies.
//
// **Disclaimer:**
// This indicator suite is intended for educational and analytical purposes only and does not constitute financial advice. Trading involves significant risk, and past performance is not indicative of future results. Always conduct your own analysis or consult a qualified financial advisor before making trading decisions.
Stochastics Confluences 4 in 1Description of the Pine Script:
This script plots the Full Stochastic indicator for four different time periods, and highlights conditions where potential buy or sell signals can be identified. The Stochastic indicator measures the position of the current closing price relative to the range of high and low prices over a defined period, helping traders identify overbought and oversold conditions.
Key Features:
Stochastic Calculation for 4 Different Periods:
The script calculates the Stochastic for four separate lookback periods: 9, 14, 40, and 60 bars.
Each Stochastic value is smoothed by a Simple Moving Average (SMA) to reduce noise and provide a clearer signal.
Visual Representation:
It plots each Stochastic value on the chart using different colors, allowing the user to see how the different periods of the indicator behave relative to each other.
Horizontal lines are drawn at 80 (Upper Bound) and 20 (Lower Bound), commonly used to identify overbought and oversold regions.
Highlighting Buy and Sell Conditions:
Green Highlight (Potential Buy Signal):
When all four Stochastic values (for the four different periods) are below 20, this suggests that the asset is in an oversold condition across multiple timeframes. The green background highlight appears when the Stochastic lines converge below 20, indicating a potential buy signal, as the price may be preparing to move upward from an oversold state.
Red Highlight (Potential Sell Signal):
When all four Stochastic values are above 80, the asset is in an overbought condition across multiple timeframes. The red background highlight appears when the Stochastic lines converge above 80, indicating a potential sell signal, as the price may soon reverse downward from an overbought state.
How to Interpret the Signals:
Buy Signals (Green Highlight):
When the chart is highlighted in green, it means the Stochastic indicators for all four periods are below 20, signaling that the asset is oversold and may be nearing a potential upward reversal. This condition suggests a possible buying opportunity, especially when other indicators confirm the potential for an upward trend.
Sell Signals (Red Highlight):
When the chart is highlighted in red, it indicates that the Stochastic indicators for all four periods are above 80, meaning the asset is overbought. This condition signals a possible downward reversal, suggesting a potential selling opportunity if the price begins to show signs of weakness.
By using this script, traders can visually identify periods of strong confluence across different timeframes when the Stochastic indicators are in extreme oversold or overbought conditions, which are traditionally seen as strong buy or sell signals.
This approach helps filter out weaker signals and focuses on moments when all timeframes align, increasing the probability of a successful trade.
Z-Score Pairs TradingTitle: Z-Score Pairs Trading Indicator
Description:
This indicator implements a Z-score based pairs trading strategy, allowing traders to identify potential statistical arbitrage opportunities between two selected assets.
Key Features:
- Calculates Z-score for the price difference between any two user-selected symbols
- Visualizes Z-score with customizable thresholds for signals
- Generates long and short signals based on extreme Z-score values
- Adaptable to various markets including stocks, ETFs, and commodities
How It Works:
1. The indicator calculates the price difference between two selected symbols.
2. It then computes the Z-score of this difference, showing how far the current spread deviates from its historical average.
3. When the Z-score exceeds set thresholds (default ±2), the indicator generates trading signals.
Settings:
- Symbol A and Symbol B: Select any two tradable symbols to compare
- Lookback Period: Number of periods for calculating the moving average and standard deviation
Interpretation:
- Z-score above 2: Potential short signal (pair is likely overextended)
- Z-score below -2: Potential long signal (pair is likely oversold)
- Z-score between -2 and 2: Normal trading range, no signals
Visual Aids:
- Blue line: Z-score
- Dashed lines: Threshold levels at 0, ±1, and ±2
- Green triangles: Long signals
- Red triangles: Short signals
Disclaimer:
This indicator is for educational and research purposes only. Trading carries a high level of risk. Always conduct your own analysis and manage your risk appropriately before entering any trade.
Made by @marekfleisi
Risk Matrix [QuantraSystems]Risk Matrix
The Risk Matrix is a sophisticated tool that aggregates a variety of fundamental inputs, primarily external (non-crypto) market data is used to assess investor risk appetite. By combining external macroeconomic factors and proxies for liquidity data with specific signals from the cryptomarket - the Risk Matrix provides a holistic view of market risk conditions. These insights are designed to help traders and investors make informed decisions on when to adopt a risk-on or risk-off approach.
Core Concept
The Risk Matrix functions as a dynamic risk assessment tool that integrates both fundamental and technical market indicators to generate an aggregated Z-score. This score helps traders to identify where the market is in a risk-off or risk-on state, The system provides both binary risk signals and a more nuanced “risk seasonality” mode for deeper analysis.
Key Features
Global Liquidity Aggregate - The Liquidity score is a custom measure of global liquidity, built by combining a variety of traditional financial metrics. These include data from central bank balance sheets, reverse repo operations and credit availability. This data is sourced from organizations such as the U.S. Federal Reserve, the European Central Bank, and the People’s Bank of China. The purpose of this aggregate is to gauge how much liquidity is available in the global financial system - which often correlates with risk sentiment. Rising liquidity tends to boost risk-on appetite, while liquidity contractions signal increased caution (risk-off) in the markets. The data sources used in this global liquidity aggregate include:
- U.S. Commercial Bank Credit data
- Federal Reserve balance sheet and reverse repo operations
- Liquidity from major central banks including the Fed, Bank of Japan, ECB, and PBoC
- Asset performance from major global financial indices such as the S&P 500, TLT, DXY (U.S. Dollar Index), MOVE (bond market volatility), and commodities like gold and oil.
Other key Z-scores (measured individually) - The Risk Matrix also incorporates other major Z-scores that represent different facets of the financial markets:
- Collateral Risk - A measure of US bond volatility, where higher values indicate higher interest rate risk - leading to potential market instability and cautious market behaviors.
- Stablecoin Dominance - The dominance of stablecoins in the crypto markets - which can signal risk aversion the total capital allocated to stables increases relative to other cryptocurrencies.
- US Currency Strength - The U.S. Dollar Index Z-score reflects currency market strength, with higher values typically indicating risk aversion as investors sell more volatile assets and flock to the dollar.
- Trans-pacific Monetary Bias - Signals capital flow and monetary trends that link between the East and West, heavily influencing global risk sentiment.
- Total - A measure of the total cryptocurrency market cap, signaling broader risk sentiment with the crypto market.
Neural Network Synthesis - The NNSYNTH component adds a machine learning inspired layer to the Risk Matrix. This custom indicator synthesizes inputs from various technical indicators (such as RSI, MACD, Bollinger Bands, and others) to generate a composite signal that reflects the health of the cryptomarket. While highly complex in its design, the NNSYNTH ultimately helps detect market shifts early by synthesizing multiple signals into one cohesive output. This score is particularly useful for gauging momentum and identifying potential turning points in market trends. Because the NNSYNTH is a closed source indicator, and it is included here, the Risk Matrix by extension is a closed source indicator.
How it Works
Z-score Aggregation - The Risk Matrix computes a final risk score by aggregating several Z-scores from different asset classes and data sources, all of which contribute proportionally to the overall market risk assessment. Each input is equally weighted - normalization allows for direct comparisons across global liquidity trends, currency fluctuations, bond market volatility and crypto market conditions. Furthermore, this system employs multi-calibration aggregation - where each individual matrix is itself an aggregate of multiple Z-scores derived from various timeframes. This ensures that each matrix captures a distinct average across different time horizons before being combined into the overall Risk Matrix. This layered, multi timeframe approach enhances the precision and robustness of the final Z-score.
Risk-On / Risk-Off Mode - The system’s binary mode provides a clear Risk On and Off signal. This nature of this signal is determined by the behavior of the Z-score relative to the midline, or Standard Deviation Bands, depending on specific conditions:
Risk-On is signaled when the aggregated final Z-score crosses above 0. However, in extreme oversold conditions, Risk-On can trigger early if the upper standard deviation band falls below the zero line. In such cases, the Risk-On signal is triggered when the z-score crosses the upper standard deviation band - without waiting to cross the midline.
Risk-Off is signaled when the final Z-score moves below 0. Similarly, Risk-Off can also be triggered early if the lower standard deviation band rises above the midline. In this instance, Risk-Off is triggered when the Z-score crosses below the lower band.
Risk Seasonality Mode - This mode offers a more gradual transition between risk states, measuring the change in the Z-score to visualize the shifts in risk appetite over time. It's useful for traders seeking to understand broader market cycles and risk phases. The seasonality view breaks down the market into the following phases:
Risk-On - High risk appetite where risk/cyclical markets are generally bullish.
Weakening - Markets showing signs of cooling off, here the higher beta assets tend to sell off first.
Risk-Off - Investors pull back, and bearish sentiment prevails.
Recovery - Signs of bottoming out, potential for market re-entry.
Component Matrices - Each individual Z-score is visualized as part of the component matrices - scaled to a 3 Sigma range. These component matrices allow traders to view how each data source is contributing to the overall risk assessment in real time - offering transparency and granularity.
Visuals and UI
Main Risk Matrix - The aggregated Z-Score is displayed saliently in the main risk matrix. Traders and investors can quickly see what season the Risk Matrix is signaling and adjust their strategies accordingly.
Overview Table - A detailed overview table shows the current confirmed Z-scores for each component, along with values from 2, and 3 bars back. This helps traders spot trends and the rate of change (RoC) between signals, offering additional insights for shorter-term risk management.
Customizability - Users can customize the visual elements of the matrix, including color palettes, table sizes, and positions. This allows for optimal integration into any trader’s existing workspace.
Usage Summary
The Risk Matrix is an incredibly versatile tool. It is especially valuable as a means of achieving a cross-market view of risk, incorporating both crypto-specific and macroeconomic factors. Some key use cases include:
Adjusting Capital Allocation Based on Risk Seasons - Traders can use the Risk Matrix to adjust their capital allocation dynamically. During Risk-On periods, they might increase exposure to long positions, capitalizing on stronger market conditions. Conversely, during Risk-Off periods, traders could reduce or hedge long positions and potentially scale up short positions or move into safer assets.
Complementing Other Trading Systems - The Risk Matrix can work alongside other technical systems to provide context to market moves. For instance, a trend-following strategy might suggest an entry, but the Risk Matrix could be used to verify whether the broader market conditions support this trade. If the Matrix is in a Risk-Off period, a trader might opt for more conservative trade sizes or avoid the trade entirely.
This flexibility allows traders to adjust their strategies and portfolio risk dynamically, enhancing decision making based on broader market conditions - as indicated by external macroeconomic factors, liquidity, and risk sentiment.
Important Note
The Risk Matrix always uses the most up-to-date data available, ensuring analysis reflects the latest market conditions and macroeconomic inputs. In rare cases, governments or financial institutions revise past data - and the Risk Matrix will adjust accordingly. This behavior can only be seen in the Liquidity Matrix. and can affect the final score. While this is uncommon, it highlights the benefit of using a system that adapts in real-time, incorporating the most accurate and current information to enhance decision making processes.
Fear Greed Zones by Relative Strength IndexThis is a visual modification of the relative Strength Index (RSI) to express extreme areas as fear and greed Zones.
// Input
rsiLength = input.int(14, "RSI Length", minval=1)
// RSI calculation
rsi = ta.rsi(close, rsiLength)
FEAR GREED ZONES
The "Fear Greed Zones Script" indicator is designed to help traders identify psychological levels of fear and greed in the market by utilising relative strength index. It primarily utilises the Relative Strength Index of price to gauge market sentiment, with the following key features:
Color-Codes
Dark Red: Indicates a greed zone , suggesting extreme overbought conditions (high risk) and a possible price reversal downward.
Dark Green: Represents a fear zone, indicating extreme oversold conditions (low risk) and potential for price reversal upward.
Yellow: Serves as a neutral zone with medium risk.
Usage
Market Sentiment Analysis: Traders can use the fear and greed zones to assess overall market sentiment, aligning their strategies with prevailing emotional biases. This helps in identifying potential entry and exit points based on market psychology.
Risk Management: Understanding fear or greed influences market behavior and allows traders to manage their risk more effectively with the knowledge of high or low risk areas; as they can anticipate potential reversals or continuations in price trends.
Conclusion
The "Fear Greed Zones" Script is a valuable tool for traders looking to leverage market psychology. By clearly identifying areas where fear or greed may be influencing price movements, it aids in making more informed trading decisions.
Money Wave Script (Visual Adaptive MFI)This Script is a visual modification of the Money Flow Index (MFI)
//@version=5
indicator(title="Money Flow Index", shorttitle="MFI", format=format.price, precision=2, timeframe="", timeframe_gaps=true)
length = input.int(title="Length", defval=14, minval=1, maxval=2000)
src = hlc3
mf = ta.mfi(src, length)
plot(mf, "MF", color=#7E57C2)
overbought=hline(80, title="Overbought", color=#787B86)
hline(50, "Middle Band", color=color.new(#787B86, 50))
oversold=hline(20, title="Oversold", color=#787B86)
fill(overbought, oversold, color=color.rgb(126, 87, 194, 90), title="Background")
This Money Wave Script is culled from. the Money Flow Index with visual representation to help traders identify money flow. In addition, the waves can be smoothened. Here’s a detailed overview based on its functionality, color coding, usage, risk management, and a concluding summary.
Functionality
The Money Wave Script operates as an oscillator that measures the inflow and outflow of money into an asset over a specified period. It calculates the MFI by considering both price and volume, which allows it to assess buying and selling pressures more accurately than traditional indicators that rely solely on price data.
Color Coding
The indicator employs a color-coded scheme to enhance visual interpretation:
Green Area: Indicates bullish conditions when the normalized Money wave is above zero, suggesting buying pressure.
Red Area: Indicates bearish conditions when the normalized Money wave is below zero, suggesting selling pressure.
Background Colors: The background changes to green when the MoneyWave exceeds the upper threshold (overbought) and red when it falls below the lower threshold (oversold), providing immediate visual cues about market conditions.
Usage
Traders utilize the Money Wave indicator in various ways:
Identifying Overbought and Oversold Levels: By observing the MFI readings, traders can determine when an asset may be overbought or oversold, prompting potential entry or exit points.
Spotting Divergences: Traders look for divergences between price and the MFI to anticipate potential reversals. For example, if prices are making new highs but the MFI is not, it could indicate weakening momentum.
Trend Confirmation: The indicator can help confirm trends by showing whether buying or selling pressure is dominating.
Customizable Settings: Users can adjust parameters such as the MFI length , Smoothen index and overbought/oversold thresholds to tailor the indicator to their trading strategies.
Conclusion
The Money Wave indicator is a powerful tool for traders seeking to analyze market conditions based on the flow of money into and out of assets. Its combination of price and volume analysis, along with clear visual cues, makes it an effective choice for identifying overbought and oversold conditions, spotting divergences, and confirming trends.
TEMA For Loop [Mattes]The TEMA For Loop indicator is a powerful tool designed for technical analysis, combining the Triple Exponential Moving Average (TEMA) with a custom scoring mechanism based on a for loop. It evaluates price trends over a specified period, allowing traders to identify potential entry and exit points in the market. This indicator enhances decision-making by providing visual cues through dynamic candle coloring, reflecting market sentiment and trends effectively.
Technical Details:
Triple Exponential Moving Average (TEMA):
- TEMA is known for its responsiveness to price changes, as it reduces lag compared to traditional moving averages. The TEMA calculation employs three nested Exponential Moving Averages (EMAs) to produce a smoother trend line, which helps traders identify the direction and momentum of the market.
Scoring Mechanism:
- The scoring mechanism is based on a custom for loop that compares the current TEMA value to previous values over a specified range. The loop counts how many previous values are less than the current value, generating a score that reflects the strength of the trend:
- A higher score indicates a stronger upward trend.
- A lower (negative) score suggests a downward trend.
Threshold Levels:
- Upper Threshold: A score above this level signals a potential long entry, indicating strong bullish momentum.
- Lower Threshold: A score below this level indicates a potential short entry, suggesting bearish sentiment.
>>>These thresholds are adjustable, allowing traders to fine-tune their strategy according to their risk tolerance and market conditions.
Signal Logic:
- The indicator provides clear signals for entering long or short positions based on the score crossing the defined thresholds.
>>Long Entry Signal: When the smoothed score crosses above the upper threshold.
>>Short Entry Signal: When the smoothed score crosses below the lower threshold.
Why This Indicator Is Useful:
>>> Enhanced Decision-Making: The TEMA For Loop indicator offers traders a clear and objective view of market trends, reducing the emotional aspect of trading. By visualizing bullish and bearish conditions, it assists traders in making timely decisions.
>>> Customizable Parameters: The ability to adjust TEMA period, thresholds, and other settings allows traders to tailor the indicator to their specific trading strategies and market conditions.
Visual Clarity: The integration of dynamic candle coloring provides immediate visual cues about the prevailing trend, making it easier for traders to spot potential trade opportunities at a glance.
The TEMA For Loop - Smoothed with Candle Colors indicator is a sophisticated trading tool that utilizes TEMA and a custom scoring mechanism to identify and visualize market trends effectively. By employing dynamic candle coloring, traders gain immediate insights into market sentiment, enabling informed decision-making for entry and exit strategies. This indicator is designed for traders seeking a systematic approach to trend analysis, enhancing their trading performance through clear, actionable signals.
Relative Strength Price Oscillator Indicator (RS PPO)Percentage Price Oscillator (PPO)
The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average. As with its cousin, MACD, the Percentage Price Oscillator is shown with a signal line, a histogram and a centerline. Signals are generated with signal line crossovers, centerline crossovers, and divergences.
PPO readings are not subject to the price level of the security and the PPO values for different securities can be compared, regardless of the price of the security.
Relative Strength (RS)
Relative strength is a strategy used in momentum investing and focuses on investing in stocks or other securities that have performed well relative to the market as a whole or to a relevant benchmark.
Chart
In the chart, Microsoft stock (MSFT) is plotted against the VanEck Semiconductor ETF (SMH).
In the example on the left, from the negative values of the RS PPO it can be seen that MSFT, although trending upward, is losing out in negative terms to the SMH etf.
In the example on the right, during a correction phase with a downward price trend, Microsoft held up relatively well compared to the Van Eck Semiconductor etf.
